Apparatus and method for motor function characterization

ABSTRACT

Analysis of keystroke dynamics performed by an individual can be used for assessment and monitoring of the individual&#39;s motor function. Keystroke events related to a user pressing one or more keys on a keyboard or regions on a touch screen may be analyzed to identify a plurality of distributions of keystroke event intervals. The plurality of distributions may be analyzed to identify one or more features indicative of variation among the distributions and indicative of the user&#39;s motor function. Monitoring of a user&#39;s motor function may include comparing a value for a feature for one plurality of distribution to a second value for the same feature for another plurality of distributions.

RELATED APPLICATIONS

This application claims priority under 35 U.S.C. §119(e) to U.S.Provisional Application Ser. No. 61/969,940, filed on Mar. 25, 2014,which is herein incorporated by reference in its entirety.

BACKGROUND

Neurological disease and motor impairment underlie many diseases andconditions. The causes and manifestations of these conditions anddiseases are diverse and numerous. Examples of causes of psychomotorimpairment are: onset of neurological illnesses (such as Alzheimer,Parkinson's disease, traumatic brain injuries, attention deficitdisorders), motor illnesses (such as osteoarthritis), psychiatricconditions (such as depression, anxiety, psychosis, personalitydisorders), developmental disorders, age, licit and illicit drugs(alcohol included), fatigue, stress, sleepiness, dehydration. Therecurrently is not a single mechanism for detecting and characterizingmotor impairment arising from different sources.

SUMMARY

One type of embodiment is directed to a method of characterizing motorfunction of a user by analyzing an input by the user to a user interfaceof at least one computing device. The method comprises receiving asequence of keystroke events indicating that the user pressed at least aportion of the user interface over a time duration, determining, by atleast one processor, a plurality of distributions of keystroke eventintervals over at least some of the time duration, wherein eachdistribution of the plurality of keystroke distributions corresponds toa portion of the time duration, the plurality of distributions ofkeystroke event intervals comprises a first distribution relating tokeystroke events included in a first portion of the time duration and asecond distribution relating to keystroke events included in a secondportion of the time duration, wherein determining a distribution of theplurality of distributions comprises identifying time intervals betweenkeystroke events that occur within a corresponding portion of the timeduration, and determining a stability of the motor function of the userat least in part by analyzing at least the first distribution and thesecond distribution to determine variation of keystroke event intervalsover the time duration.

In some embodiments, receiving the sequence of keystroke eventscomprises receiving a sequence of a plurality of key selection events,and identifying time intervals between keystroke events comprisesidentifying time intervals between key selection events of the pluralityof key selection events. In some embodiments, determining a stability ofthe motor function of the user comprises analyzing at least the firstdistribution and the second distribution to determine a measure ofvariation in width of at least the first distribution and the seconddistribution. In some embodiments, analyzing at least the firstdistribution and the second distribution comprises: calculating at leastone feature of the first and second distributions and determiningvariation of the at least one feature between the first and seconddistributions. In some embodiments, calculating the at least one featurecomprises calculating a median keystroke event interval for eachdistribution of the plurality of distributions and analyzing at leastthe first and second distributions comprises calculating an averagemedian keystroke event interval by averaging the median keystroke eventintervals for each distribution. In some embodiments, calculating the atleast one feature comprises comparing at least the first distribution tothe second distribution to obtain a degree of similarity indicative ofthe variation among the plurality of distributions. In some embodiments,calculating the at least one feature comprises comparing eachdistribution of the plurality of distributions to each distribution ofthe plurality of distributions to obtain a degree of similarityindicative of the variation among on the plurality of distributions.

In some embodiments, determining a distribution of the plurality ofdistributions comprises identifying a plurality of keystroke timeintervals between keystroke events related to the user pressing a key ofthe user interface. In some embodiments, determining a distribution ofthe plurality of distributions comprises identifying a plurality ofkeystroke time intervals between keystroke events related to the userpressing a key of the user interface and a subsequent key of the userinterface. In some embodiments, determining a distribution of theplurality of distributions comprises identifying a plurality ofkeystroke time intervals between keystroke events related to the userpressing a first key of the plurality of keys and a second key of theplurality of keys before releasing the first key. In some embodiments,the first portion of the time duration and the second portion of thetime duration are non-overlapping portions of the time duration.

In some embodiments, receiving the sequence of keystroke eventscomprises receiving a sequence of keystroke events input by the userwhile interacting with a plurality of different applications executingon the at least one computing device. In some embodiments, receiving thesequence of keystroke events comprises receiving a sequence of keystrokeevents input by the user with a plurality of processes executing on theat least one computing device.

In some embodiments, the method further comprises receiving a secondsequence of keystroke events indicating that the user pressed at least aportion of the user interface over a second time duration, determining,by the at least one processor, a second plurality of distributions ofkeystroke event intervals over at least some of a second time duration,wherein each distribution of the second plurality of keystrokedistributions corresponds to a portion of the second time duration, thesecond plurality of distributions of keystroke event intervals comprisesa third distribution relating to keystroke events included in a firstportion of the second time duration and a fourth distribution relatingto keystroke events included in a second portion of the second timeduration, wherein determining a second distribution of the plurality ofdistributions comprises identifying time intervals between keystrokeevents that occur within a corresponding portion of the second timeduration, and determining a second stability of the motor function ofthe user at least in part by analyzing at least the third distributionand the fourth distribution to determine variation of keystroke eventintervals over the second time duration.

In some embodiments, the method further comprises identifying a change,if any, in the user's motor functions between the time duration and thesecond time duration by comparing the plurality of distributions to thesecond plurality of distributions. In some embodiments, a differencebetween the time duration and the second time duration is a time periodover days. In some embodiments, a difference between the time durationand the second time duration is a time period over months.

In some embodiments, a difference between the time duration and thesecond time duration is a time period over years.

In some embodiments, identifying a change in the user's motor functionsbetween the time duration and the second time duration comprisesdetermining a first feature related to variation in distribution spreadamong the plurality of distributions and a second feature related tovariation in distribution spread among the second plurality ofdistributions and comparing at least the first feature and the secondfeature. In some embodiments, identifying a change in the user's motorfunctions between the time duration and the second time durationcomprises determining a difference between at least the first featureand at least the second feature. In some embodiments, identifying achange in motor function between the time duration and the second timeduration comprises identifying a measure of an increase in hold timewhen the user presses a key based on comparing the plurality ofdistributions and the second plurality of distributions. In someembodiments, the identified change in the user's motor functionindicates that the user's motor function is impaired.

In some embodiments, the method further comprises determining an averagevalue for the plurality of distributions by averaging a plurality ofmedian values, wherein each median value is a median keystroke eventinterval for a distribution of the plurality of distributions,determining a second average value for the second plurality ofdistributions by averaging a plurality of median values, wherein eachmedian value is a median keystroke event interval for a distribution ofthe second plurality of distributions, determining a degree ofsimilarity indicative of a variation among the plurality ofdistributions for the plurality of distributions by comparing theplurality of distributions to each other, determining a second degree ofsimilarity indicative of a variation among the plurality ofdistributions for the second plurality of distributions by comparing thesecond plurality of distributions to each other, identifying a featurevector based on the average value and the degree of similarity, andidentifying a second feature vector based on the second average valueand the second degree of similarity, wherein identifying a change in theuser's motor functions between the time duration and the second timeduration comprises determining a difference between the feature vectorand the second feature vector.

In some embodiments, the method further comprises determining a firstquantile and a second quantile for a distribution of the plurality ofdistributions, identifying key stroke event intervals in thedistribution as outliers based on the first quantile, calculating anumber of outliers in the distribution based on the identified at leastone outlier, normalizing the number of outliers by a number of keypresses for the distribution, calculating a difference between the firstquantile and the second quantile, calculating a standard deviation ofthe outliers, calculating a value based on at least a portion of acovariance of the plurality of distributions, and determining a featurevector based on the number of outliers, the difference, the standarddeviation, and the value.

In some embodiments, the method further comprises determining, via amachine learning classifier, a second feature vector based a seconddistribution of the plurality of distributions. In some embodiments, theuser interface is a physical keyboard or a virtual keyboard.

In some embodiments, the method further comprises presenting, via asecond user interface, a report providing a characterization of theuser's motor functions as a condition based on the stability of theuser's motor function.

In some embodiments, the method further comprises determining a featurefrom information related to measurements performed by sensor incommunication with the computing device.

Another type of embodiment is directed to at least one computer-readablestorage medium storing computer-executable instructions that, whenexecuted, perform a method of characterizing motor function of a user byanalyzing an input by the user to a user interface of at least onecomputing device. The method comprises receiving a sequence of keystrokeevents indicating that the user pressed at least a portion of the userinterface over a time duration, determining, by at least one processor,a plurality of distributions of keystroke event intervals over at leastsome of the time duration, wherein each distribution of the plurality ofkeystroke distributions corresponds to a portion of the time duration,the plurality of distributions of keystroke event intervals comprises afirst distribution relating to keystroke events included in a firstportion of the time duration and a second distribution relating tokeystroke events included in a second portion of the time duration,wherein determining a distribution of the plurality of distributionscomprises identifying time intervals between keystroke events that occurwithin a corresponding portion of the time duration, and determining astability of the motor function of the user at least in part byanalyzing at least the first distribution and the second distribution todetermine variation of keystroke event intervals over the time duration.

Another type of embodiment is directed to a system for characterizingmotor function of a user by analyzing an input by the user to a userinterface of at least one computing device. The system comprises atleast one processor configured to receive a sequence of keystroke eventsindicating that the user pressed at least a portion of the userinterface over a time duration and at least one storage medium storingprocessor-executable instructions that, when executed by the at leastone processor, perform a method comprising: determining a plurality ofdistributions of keystroke event intervals over at least some of thetime duration, wherein each distribution of the plurality of keystrokedistributions corresponds to a portion of the time duration, theplurality of distributions of keystroke event intervals comprises afirst distribution relating to keystroke events included in a firstportion of the time duration and a second distribution relating tokeystroke events included in a second portion of the time duration,wherein determining a distribution of the plurality of distributionscomprises identifying time intervals between keystroke events that occurwithin a corresponding portion of the time duration, and determining astability of the motor function of the user at least in part byanalyzing at least the first distribution and the second distribution todetermine variation of keystroke event intervals over the time duration.

Another type of embodiment is directed to a method of characterizingmotor function of a user by analyzing an input by the user to a userinterface of at least one computing device. The method comprisesreceiving a sequence of keystroke events indicating that the userpressed at least a portion of the user interface over a time durationwhile the user is interacting with a plurality of different applicationsexecuting on the at least one computing device, determining, by at leastone processor, a plurality of distributions of keystroke event intervalsover at least some of the time duration, wherein each distribution ofthe plurality of keystroke distributions corresponds to a portion of thetime duration and determining a distribution of the plurality ofdistributions comprises identifying time intervals between keystrokeevents that occur within a corresponding portion of the time duration,and determining a stability of the motor function of the user at leastin part by analyzing at least the first distribution and the seconddistribution to determine variation of keystroke event intervals overthe time duration.

Another type of embodiment is directed to a method of characterizingmotor function of a user by analyzing an input by the user to a userinterface of at least one computing device. The method comprisesreceiving a sequence of keystroke events indicating that the userpressed at least a portion of the user interface over a time durationwith a plurality of processes executing on the at least one computingdevice, determining, by at least one processor, a plurality ofdistributions of keystroke event intervals over at least some of thetime duration, wherein each distribution of the plurality of keystrokedistributions corresponds to a portion of the time duration anddetermining a distribution of the plurality of distributions comprisesidentifying time intervals between keystroke events that occur within acorresponding portion of the time duration, and determining a stabilityof the motor function of the user at least in part by analyzing at leastthe first distribution and the second distribution to determinevariation of keystroke event intervals over the time duration.

Another type of embodiment is directed to an apparatus comprising a userinterface and control circuitry configured to perform a methodcomprising: receiving a sequence of keystroke events indicating that theuser pressed at least a portion of the user interface over a timeduration; determining a plurality of biosignatures indicative of theuser's motor function at different times by determining, for abiosignature of the plurality of biosignatures, a plurality ofdistributions of keystroke event intervals over at least some of thetime duration, wherein each distribution of the plurality of keystrokedistributions corresponds to a portion of the time duration; andmonitoring motor function in the user by tracking a condition of theuser's motor function over time based on comparing a first biosignatureof the plurality of biosignatures with a second biosignature.

In some embodiments, comparing the first biosignature of the pluralityof biosignatures with the second biosignature comprises identifying afirst stability level for the first biosignature and a second stabilitylevel for the second biosignature and determining a difference betweenthe first stability level and the second stability level. In someembodiments, determining the difference between the first stabilitylevel and the second stability level indicates a decrease in stabilityand the tracked condition indicates impairment of the user's motorfunction over time. In some embodiments, the control circuitry isfurther configured to issue a report related to the tracked condition tothe user. In some embodiments, the control circuitry is furtherconfigured to issue a report related to the tracked condition to amedical provider. In some embodiments, the second biosignature is abiosignature of the plurality of biosignatures corresponding to anearlier time than the first biosignature. In some embodiments, thesecond biosignature is associated with a second user having motorfunction that is identified as being unimpaired.

In some embodiments, the user interface is a user interface of a firstcomputing device, the control circuitry is a component of at least onesecond computing device different from the first computing device, andthe first computing device and the at least one second computing deviceare adapted to communicate over at least one network.

Another type of embodiment is directed to a method of characterizingmotor function of a user by analyzing an input by the user to a userinterface of at least one computing device. The method comprisesreceiving a sequence of keystroke events indicating that the userpressed at least a portion of the user interface over a time duration,determining a plurality of biosignatures indicative of the user's motorfunction at different times by determining, for a biosignature of theplurality of biosignatures, a plurality of distributions of keystrokeevent intervals over at least some of the time duration, wherein eachdistribution of the plurality of keystroke distributions corresponds toa portion of the time duration, and monitoring motor function in theuser by tracking a condition of the user's motor function over timebased on comparing a first biosignature of the plurality ofbiosignatures with a second biosignature.

In some embodiments, the method further comprises identifying impairmentin the user's motor function over time based on monitoring the user'smotor function and comparing a characterization of the user's motorfunction to characterizations of motor function associated with aplurality of conditions and, when the characterization of the user'smotor function matches a characterization associated with a condition,storing an indication that the user may have the condition.

In some embodiments, the method further comprises outputting a messagecontaining an indication that the user may have impaired motor function.In some embodiments, the method further comprises outputting a messageindicating that the user may have a neurological disorder. In someembodiments, the method further comprises outputting a messageindicating that the user may have a condition of sleep deprivation. Insome embodiments, outputting a message indicating that the user may havea condition of intoxication. In some embodiments, the method furthercomprises outputting a message indicating that the user may have acognitive impairment. In some embodiments, the method further comprisesoutputting a message indicating that the user may have a condition ofarthritis. In some embodiments, the method further comprises outputtinga message indicating that the user may have a condition of sleepinertia.

Another type of embodiment is directed to a method for characterizing apsychomotor impairment in a user. The method comprises detecting aplurality of keystroke events as a background task while the user isinteracting with a device sensitive to touch, determining time intervalsassociated with the keystroke events, and characterizing a psychomotorimpairment in the user based on the time intervals associated with thekeystroke events.

Another type of embodiment is directed to a method for early detectionof a neurological disease in a user. The method comprises detecting aplurality of keystroke events while the user is interacting with adevice sensitive to touch, determining time intervals associated withthe keystroke events, and identifying the presence of a neurologicaldisease in the user prior to diagnosis of physical symptoms in the user.

Another type of embodiment is directed to an electronic devicecomprising a tactile interface for receiving a plurality of keystrokes,a processor configured to receive user input from the tactile interfaceon the plurality of keystrokes, and a storage medium storing processorexecutable instructions that when executed by the processor perform amethod comprising determining a plurality of distributions of keystrokeevent intervals over at least some of the time duration, wherein eachdistribution of the plurality of keystroke distributions corresponds toa portion of the time duration, the plurality of distributions ofkeystroke event intervals comprises a first distribution relating tokeystroke events included in a first portion of the time duration and asecond distribution relating to keystroke events included in a secondportion of the time duration, wherein determining a distribution of theplurality of distributions comprises identifying time intervals betweenkeystroke events that occur within a corresponding portion of the timeduration; and analyzing at least the first distribution and the seconddistribution to determine variation of keystroke event intervals overthe time duration.

In some embodiments, the electronic device is a computer. In someembodiments, the electronic device is a tablet. In some embodiments, theelectronic device is a touch screen.

BRIEF DESCRIPTION OF DRAWINGS

The accompanying drawings are not intended to be drawn to scale. In thedrawings, each identical or nearly identical component that isillustrated in various figures is represented by a like numeral. Forpurposes of clarity, not every component may be labeled in everydrawing. In the drawings:

FIG. 1 is a schematic of an exemplary system that performscharacterization of a user's motor function.

FIG. 2 is a schematic illustrating different keystroke dynamicvariables.

FIG. 3A is a graph illustrating an exemplary plot of hold time values asa function of time.

FIG. 3B-D are graphs of exemplary distributions of hold time values.

FIG. 3E is a visual representation of an exemplary plurality ofdistributions.

FIG. 4 are visual representations of exemplary pluralities ofdistributions that include treated and untreated individuals withParkinson's disease and a control individual.

FIG. 5 are visual representations of exemplary pluralities ofdistributions for individuals in a rested state and a state of sleepinertia.

FIG. 6 are visual representations of exemplary self-similarity matricesfor different pluralities of distributions.

FIGS. 7A-D are circular histograms illustrating differences in a featurevector for two different pluralities of distributions corresponding toindividuals in rested and sleep inertia states.

FIGS. 7E and 7F are plots illustrating variation in two features fordifferent pluralities of distributions corresponding to data obtainedfrom individuals during day and night in a sleep inertia study.

FIGS. 8A and 8B illustrate representative profiles for scores determinedby a machine learning classier for individuals with Parkinson's diseaseand control individuals.

FIG. 9 is a block diagram of an exemplary computer system on which someembodiments may be implemented.

DETAILED DESCRIPTION

The inventors have recognized and appreciated that operating adiagnostic tool to produce biosignatures indicative of a person's motorfunction, such as by analyzing keystroke events to determine adistribution of keystroke event intervals, may provide an improvedmechanism for evaluating that person's motor function and aiding in thediagnosis of neurological impairments. Such a diagnostic tool mayevaluate a motor function of a person through analyzing a sequence ofkeystroke events indicating keyboard keys, such as physical ortouchscreen keyboard keys, that the person has pressed over a period oftime. A keystroke event, as used herein, refers to the act of employingfingers on a mechanical input device (such as buttons) or surfaces ableto detect such interaction (such as touchscreens). In some particularlyadvantageous embodiments, the sequence of keystroke events may becollected while the person interacts with multiple differentapplications executing on a computing device, such as while the personis using a computing device to perform tasks unrelated to diagnosingmotor function impairment.

A person's “motor function” relates to the ways in which the person'smuscles move or ways in which a person's muscles move the person's limbsor digits. Motor function may indicate a health of a person or,conversely, a variety of conditions that may exist in the person,including neurological disorders (e.g., Parkinson's Disease,Parkinsonism, Amyotrophic Lateral Sclerosis, forms of Dementia such asAlzheimer's Disease and Mild Cognitive impairment) the person may besuffering from or other neurological impairments such as brain injury(e.g., concussions), motor illnesses such as osteoarthritis, psychiatricconditions such as personality disorders, depression, anxiety,psychosis, developmental disorders, transient conditions such asintoxication, fatigue, stress, and dehydration. Monitoring of a person'smotor function may therefore aid in diagnosing conditions that mayresult in impaired motor function of the person.

The inventors have therefore recognized and appreciated that, bymeasuring a person's motor function, the person's neurological statuscan be assessed and/or diagnosed. For example, at a given time, theperson's motor function may be compared to a previous indication ofmotor function obtained at an earlier time to achieve a degree of motorfunction impairment. Additionally or alternatively, the person's motorfunction may be compared to a reference person's motor function, such asa person who has been identified as unimpaired, to determine a degree ofmotor function impairment. In this manner, monitoring of a person'smotor function and the person's condition related to the person's motorfunction may be performed over time as part of assessing and/ordiagnosing conditions such as neurological conditions, arthritis, sleepdeprivation, and/or intoxication.

The inventors have recognized and appreciated that it would beparticularly advantageous if diagnostic tools were able to identifychanges in motor function early, which would in turn allow for earlyscreening of motor-comprised conditions including neurodegenerativeconditions, psychological disorders, and/or transient conditions such asintoxication.

The inventors have additionally recognized and appreciated that aperson's keystrokes while typing on a keyboard may provide an indicationof the person's motor function. The term keystroke and keyboard are usedthroughout the application to refer to the physical depressing of keyssuch as on a physical keyboard as well as the touch of a touch screen.More particularly, the inventors have recognized and appreciated thatthe dynamics associated with a person's keystrokes may be indicative ofmotor function. As a person performs a typing task, that person willpress and release individual keys of a keyboard or regions of a touchscreen that relate to letters, numbers or other functionalities.Analysis of the timing associated with the pressing and releasing ofindividual keys or regions of a screen may provide an indication of theperson's motor function. For example, specific time variables associatedwith keystroke dynamics, including hold time, press latency, flighttime, and/or release latency, may be influenced by a person's motorfunction and thus may, once analyzed, provide an indication of thatperson's motor function. In this manner, a person's user input for oneor more processing tasks may be analyzed to determine characteristics ofthe person's keystroke dynamics, and to assess the person's motorfunction.

The inventors have therefore recognized the advantages of a diagnostictool that analyzes keystroke events collected during a person'soperation of a computing device to derive time variables associated withkeystroke dynamics and that analyzes the time variables to assess aperson's motor function. By monitoring the person's user input over timeusing such a tool, changes in the time intervals may be tracked andassessed to determine potential changes in motor function over time. Thediagnostic tool may analyze the time intervals by generatingdistributions of keystroke timing information in time intervals, and bycompiling such distributions of timing intervals to form biosignaturesthat are indicative of a person's motor function. Analyzing the timinginformation set forth within one biosignature, and/or comparingbiosignatures, may enable motor function to be reliably assessed.

Moreover, the inventors have recognized and appreciated that such dataon keystroke events may be collected while individuals are interactingwith their personal computing devices, such as personal computers,mobile phones, tablet computers, or personal digital assistants whilethey perform routine tasks unrelated to diagnosis. Because individualsinteract often with electronic devices in ways that require user inputto be provided through keystroke events, a diagnostic tool that monitorsa person's keystroke dynamics while the person interacts with personalcomputing device(s) may have a lower burden on users and enable morefrequent and consistent monitoring of the person's motor function thanthough scheduled visits to a facility designated to evaluate motorfunction, such as a medical facility. This may permit more data to becaptured and analyzed over time. Accordingly, characterization of motorfunction according to techniques described herein may provide an earlierindication of motor function impairment, such as before the onset ofsymptoms indicating a higher level of impairment in motor function(e.g., tremors or stiffness in people with Parkinson's disease), than isavailable with other diagnostic tools. This may in turn allow forearlier intervention for treatment of the causes of any neurologicalimpairment.

Other electronic devices may have physical and/or virtual keys that maycollect data on keystroke events such as remote controllers, householdappliances (e.g., refrigerators, coffee machines, microwaves),industrial machines and appliances, medical electronic devices, andmotor vehicle panels (e.g., entertainments system, control panels,Global Positioning Systems). Keystroke events collected from a devicemay be used to monitor motor function of a person while the person isperforming a task and/or operating the device without altering thefunctionality of the device. As an example, a control panel of a motorvehicle may collect keystroke events related to input from a driver ofthe motor vehicle which may be analyzed to provide an indication ofmotor function impairment due to a condition such as a state of fatigueor sleep deprivation. This may allow for an intervention to deter thedriver from operating the motor vehicle.

Accordingly, described herein are embodiments of a diagnostic tool foraiding in evaluating motor function of a person by analyzing keystrokeevents resulting from input by a person to a keyboard, such as aphysical keyboard or a touchscreen keyboard. In some embodiments, theanalysis performed by the diagnostic tool may include identifying timeinformation associated with keystroke events related to a user input andidentifying keystroke event intervals, such as hold time and presslatency, from the time information. The tool may also calculatedistributions (e.g., histograms or probability density functions) ofkeystroke event intervals over portions of time, which may assist inidentifying variations in the keystroke event intervals. By analyzingdifferent features of the distributions, variation among thedistributions may be determined and provide an indication of the user'smotor function.

Lack of variation or narrow variation among distributions may indicatestability in the user's motor function, while broad variation among thedistributions may indicate instability of the user's motor function.Variation among different distributions may be identified by changingpeak values and/or spread of the distributions. As an example,distributions of times associated with a user pressing keys or a regionof a screen or “hold time” may be determined and variation among thedistributions may indicate a stability of the user's motor function. Insome embodiments, the distributions may be compared with each other toidentify a level of similarity among the distributions indicative ofstability of the user's motor function. A set of distributions acquiredover a time duration may provide a biosignature for a person throughoutthe time duration.

In some embodiments, the analysis performed by the diagnostic tool mayinclude comparing different sets of distributions acquired at differenttime durations to identify changes in stability of the user's motorfunction. In some embodiments, an average of peak values from one set ofdistributions may be compared with an average of peak values fromanother set of distributions. For example, an average value for holdtime may be compared between two different sets of distributions. Thetwo sets of distributions may be determined for time durations separatedby days, months, or years. An increase in average hold time may indicateimpairment in the person's motor function, while no or little change inaverage hold time may indicate stability in the person's motor function.

In some embodiments, one or more sensors may provide additional dataused to detect other aspects relevant to a user's motor function. Asensor may be a built-in sensor integrated as part of an electronicdevice or a separate component in communication with an electronicdevice. Measurements performed by one or more sensors of the electronicdevice may provide additional information that may be complementary tothe information acquired through analysis of keystroke events. A sensormay measure motion, orientation, position, typing pressure, and/orvarious environmental conditions. A sensor may be an accelerometer,gravity sensor, gyroscope, rotational vector sensor, orientation sensor,a magnetometer, pressure sensor, thermometer, barometer, microphone,and/or photometer. A diagnostic tool may analyze data from one or moresensors (e.g. accelerometer data, gyroscope data, typing pressure data,location data, voice data). In some embodiments, the analysis performedby the diagnostic tool may include analyzing information from one ormore sensors that is associated with keystroke events related to a userinput.

Examples of diagnostic tools operating in accordance with techniquesexplained above are described below, but it should be appreciated thatthe examples are provided merely for purposes of illustration and thatother implementations are possible.

FIG. 1 illustrates an exemplary embodiment of a system 100 for assessinga user's motor function. The system 100 includes user interface 104 thatis a component of and/or in communication with computing device 106.Using techniques described herein, the system 100 may analyze a user'sinteractions with keys or screen regions of the user interface 104.

As shown in the example of FIG. 1, user interface 104 may include aphysical keyboard having a plurality of physical keys. As an example, incases in which device 106 is a typical laptop or desktop personalcomputer, interface 104 may include a full-size physical keyboard havingkeys organized in a typewriter layout (a “QWERTY” layout) or other keyarrangement. Such a keyboard may be, for example, an external keyboardas an added component that connects to other components of the computingdevice 106 via a wired and/or wireless connection. In other cases,device 106 is implemented as another computing device, such as a mobiletelephone, handheld computer or smartphone, and the interface 104 mayinstead include a miniature keyboard integrated as part of the computingdevice 106. As is known, a keyboard is typically operated by a user'shands/fingers. User 102 would use his or her fingers to activateindividual keys of the keyboard by touching and/or pressing them.

While a physical keyboard is illustrated in FIG. 1, it should beappreciated that embodiments are not limited to operating with aparticular type of keyboard or keys. In some embodiments, the userinterface 104 may include a touchscreen instead or in addition to aphysical keyboard. A touchscreen includes a visual display on which aprogram executing on the computing device 106 can present information. Auser can provide input by touching the screen, typically with the user'sfingers or a stylus. The touchscreen responds to the input by providingan indication of where the screen was touched. Software of the interface104 and/or control software 108 of the computing device 106 maycorrelate the indication of where the user touched the touch screen towhat information was being displayed at that time. Depending on thenature of the contact with the touch screen and/or the informationdisplayed on the touch screen at the location of that contact, theoutput of the touch screen may be interpreted differently. The touchscreen, for example, may be configured with graphics representing keys.When the user contacts the touch screen at a location occupied by adisplay of a key, the electronic device may respond just like it wouldto a user input through a keyboard designating the same key. A computerconfigured in this way may be said to have a virtual keyboard. Whenthere is a virtual keyboard on the screen, a user may select a key onthe keyboard by touching the location of the key on the screen. Anapplication may interpret the touched location as keyboard inputinformation, indicating that output information associated with the keyis to be displayed, such as text characters like “B” or “L”.Additionally, a user may touch a location on the screen to select alink, such as to open an application or link. The operating system mayinterpret the touched location as navigational information, indicatingthat output information associated with touching the particular locationis to be displayed.

Regardless of the type of user interface, interface 104 may generateinformation indicating interactions of the user with the interface 104.That indication may be passed from interface 104 to control software108, which may be implemented as part of an operating system, firmware,or other suitable control software for interacting with device hardware.Control software 108 may then pass the indication of the input toapplication program 110 executing on computing device 106. In thismanner, user 102 may provide input while an application or process 110is executing on computing device 106, which may be input that the user102 provides to the application or process 110.

In some embodiments in which the keystroke events are user input to anapplication or process 110, the application or process 110 may be adiagnosis application that requests that user 102 provide input in theform of keystrokes for the purpose of testing motor function. In otherembodiments that may be particularly advantageous in some scenarios, theapplication or process 110 may be unrelated to medical testing anddiagnosis, including unrelated to testing motor function. In such cases,the application or process 110 may be related to performing one or moreprocessing tasks on computing device 106 that are personal orprofessional tasks for the user 102, such as internet browsing, documentediting or other word processing, data entry, or other applications. Insome such embodiments, the application or process 110 may be multipledifferent applications/processes that are unrelated to medical testingor diagnosis. In such cases, collection and/or analysis of user inputmay be performed as a background process, while the application(s) orprocess(es) 110 is/are executing on computing device 106.

In accordance with techniques described herein, in addition to beingpassed to the application/process 110, information regardinginteractions of a user with the interface 104 may be passed to a motorfunction characterization system 114. In some embodiments, controlsoftware 108 may pass the information on the interactions with theinterface 104 to the system 114. In other embodiments, a module 112 maycollect the information on the user input from the control software 108and/or application 110 and occasionally, periodically, or continuously(e.g., as the information is received) transmit the information on theuser input to the system 114. In some embodiments, module 112 maycollect information on measurement data from one or more sensorsintegrated in computing device 106 or in communication with computingdevice 106. In some embodiments, the one or more sensors may beintegrated in user interface 104. As an example, pressure sensors aspart of a touchscreen may measure pressure as a user touches a locationof the touchscreen such as a virtual key. The one or more sensors mayprovide information related to the user's movement, location,orientation, voice, and/or typing pressure. Module 112 may occasionally,periodically, or continuously (e.g., as the information is received)transmit information from the one or more sensors to system 114. In someembodiments, the module 112 may be a component of the control software108 (e.g., a component of an operating system) or a component of theapplication 110, such as a plug-in to the application 110. Embodimentsare not limited to implementing the module 112 in any particular manner.

In the example of FIG. 1, motor function characterization system 114includes user input analyzer 116 and motor function analyzer 118. Theuser input analyzer 116 receives data regarding a user input from thedevice 106. In some embodiments, user input analyzer 116 may receivethat data in the form of a sequence of keystroke events. A keystrokeevent may include information relating to a user pressing or releasing akey. A sequence of keystroke events may include only press events, onlyrelease events, or both. Keystroke events of the sequence may beformatted as operating system events or events output by a devicedriver, and/or may be implemented in another format containing otherinformation. As such, in embodiments in which the control software 108is implemented as an operating system, the keystroke events may beprovided as an output from operating system 108 and indicate that theuser pressed one or more keys or otherwise operated a touchscreen.

In some embodiments, the sequence of keystroke events may include asequence of multiple key selection events. A key selection event may beassociated with a user pressing keys on a keyboard, such as physical ortouchscreen keyboard. The “key down” event may be associated with a userdepressing a physical key of a physical keyboard or initiating contactwith a touchscreen. A “key up” event may be associated with a userreleasing a physical key of a physical keyboard or ending contact with atouchscreen.

The sequence of keystroke events received by the user input analyzer 116may indicate that the user pressed one or more keys or touches over atime duration, and may include time information for each of thekeystroke events. The user input analyzer 116 may analyze the keystrokeevent information, including the time information, from the sequence aspart of analyzing a user's motor function, as described in detail below.

Results of keystroke event analysis produced by the user input analyzer116 may be provided to a motor function analyzer 118. Motor functionanalyzer 118 may analyze the information provided by the user inputanalyzer 116 and produce information indicative of a motor function ofthe user 102, which may include one or more biosignatures for the user102 and/or one or more distributions of time intervals for keystrokeevents. From analyzing the biosignature(s) and/or distribution(s), themotor function analyzer 118 may generate information on whether the user102 appears to have a healthy motor function or whether the user 102appears to have an impaired motor function. The motor function analyzer118 may also, in some embodiments, be configured to produce indicationsof one or more conditions that analysis of the sequence of keystrokeevents indicates the user 102 may have, which the analyzer 118 mayoutput for review by the user 102 and/or a clinician who may diagnosethe user 102.

Motor function characterization system 114 may be implemented in anysuitable form. While motor function characterization system 114 isillustrated in FIG. 1 as separate from the computing device 106, itshould be appreciated that one or more components of motor functioncharacterization system 114 may be part of computing device 106 and/orpart of one or more other computing devices. In some embodiments, forexample, motor function characterization system 114 may be implementedas a single stand-alone machine, or may be implemented by multipledistributed machines that share processing tasks in any suitable manner,that is/are separate from the computing device 106 operated by the user102. Motor function characterization system 114 may be implemented asone or more computers; an example of a suitable computer is describedbelow. In some embodiments, motor function characterization system 114may include one or more tangible, non-transitory computer-readablestorage devices storing processor-executable instructions, and one ormore processors that execute the processor-executable instructions toperform functions described herein. The storage devices may beimplemented as computer-readable storage media (i.e., tangible,non-transitory computer-readable media) encoded with theprocessor-executable instructions; examples of suitablecomputer-readable storage media are discussed below.

Each of the processing components of motor function characterizationsystem 114, including analyzers 116, 118 may be implemented in software,hardware, or a combination of software and hardware. Componentsimplemented in software may comprise sets of processor-executableinstructions that may be executed by the one or more processors of motorfunction characterization system 114 to perform the functionalitydescribed herein. Each of user input analyzer 116 and motor functionanalyzer 118 may be implemented as a separate component of motorfunction characterization system 114 (e.g., implemented by hardwareand/or software code that is independent and performs dedicatedfunctions of the component), or any combination of these components maybe integrated into a single component or a set of distributed components(e.g., hardware and/or software code that performs two or more of thefunctions described herein may be integrated, the performance of sharedcode may be distributed among two or more hardware modules, etc.). Inaddition, any one of user input analyzer 116 and motor function analyzer118 may be implemented as a set of multiple software and/or hardwarecomponents.

Although the exemplary embodiment of FIG. 1 depicts user input analyzer116 and motor function analyzer 118 implemented together on motorfunction characterization system 114, this is only an example; in otherexamples, any or all of the components may be implemented on one or moreseparate machines, or parts of any or all of the components may beimplemented across multiple machines in a distributed fashion and/or invarious combinations. It should be understood that any such componentdepicted in FIG. 1 is not limited to any particular software and/orhardware implementation and/or configuration.

For ease of explanation, examples set forth below will be described withreference to components of the system 100 of FIG. 1. It should beappreciated, however, that embodiments are not limited to operating inthe exemplary environment of FIG. 1 or in similar environments.

As discussed above, in some embodiments an evaluation of motor functionmay include an analysis of time information associated with keystrokeevents, such as a sequence of events received by a user input analyzer116. In some embodiments, the time information may be produced fromanalysis of timestamp information associated with each keystroke eventindicating a time when a user pressed a key and when output from userinterface 104 was received by control software 108 and/or passed to userinput analyzer 116. In some embodiments, user input analyzer 116 may notreceive keystroke events associated with timestamp information and mayitself determine time information to associate with keystroke events.For example, in some embodiments the user input analyzer 116 may receivekeystroke events in real time as the events are generated in response touser input to the interface 104 and may associate a time with eachkeystroke event as the event is received by the user input analyzer 116.Timestamp information may be in any suitable format, including anabsolute time such as time of day or a relative time to a certain pointin time such as when the user began providing the user input.

From an analysis of timestamp information for keystroke events of thesequence, user input analyzer 116 may identify time intervals forcertain keystroke events and/or between certain keystroke events. Thetime intervals may identify lengths of time for the certain keystrokeevents or between keystroke events. The time intervals may correspond tokeystroke dynamics time variables such as hold time, flight time, presslatency, and release latency. Other time intervals may identify otheraspects of timing of keystroke events. In some embodiments, timeintervals may correspond to a user pressing a first key followed by asecond key before releasing the first key. The time intervals mayidentify lengths of time where the user is pressing two keys at once.

FIG. 2 illustrates a schematic of possible time variables that may beidentified by user input analyzer 116. For example, user input from auser pressing keys on a keyboard or regions on a touch screen mayprovide a sequence of key up and key down events over time, as shown inFIG. 2. Time intervals between key up and key down events may bedetermined by user input analyzer 116. In some embodiments, a keystroketime interval may be identified for a keystroke event, or between twokeystroke events that relate to a user depressing one key, such as a keydown event and a subsequent key up event. Such a keystroke time intervalmay be referred to as “hold time” and may refer to the time between whenthe user depresses that one key and when the user releases that one key.Additionally or alternatively, a keystroke time interval may beidentified between subsequent keystroke events that relate to a userswitching between keys, which may be referred to as “flight time.” Sucha keystroke time interval may refer to a time between a user releasingone key and depressing the next, and as such may correspond to a timebetween a key up event and a subsequent key down event. In someembodiments, a keystroke time interval may be identified betweenkeystroke events related to a user pressing a key followed by pressing asubsequent key. Such a time interval between a key down event and a keyup event may be referred to as “press latency,” while a time intervalbetween a key up event and a key down event may be referred to as“release latency.”

Other suitable time intervals between keystroke events than the timeintervals discussed in FIG. 2 may be analyzed by user input analyzer116. A sequence of keystroke events may include adjacent “key up” eventsand/or adjacent “key down” events. In some embodiments, a time intervalmay correspond to a length of time between a “key up” event and another“key up” event that immediately follows the first “key up” event in thesequence, and/or two “key down” events that are similarly adjacent inthe sequence. As an example, a user may press a key prior to releasing aprevious key, creating a sequence of keystroke events with two “keydown” events, with one immediately following the other in the sequence.This may correspond, for example, to the user pressing two keys at once,such as when a user inadvertently strikes two keys or when a userpresses and holds one key while pressing another key, such as when theuser is pressing a combination of keys, or when the user means to striketwo keys in succession but strikes the next key before releasing thefirst key.

Event time intervals identified by user input analyzer 116 may beprovided to motor function analyzer 118 to characterize motor functionof the user providing the user input. In some embodiments, the motorfunction analyzer 118 may determine distributions of keystroke eventintervals identified by user input analyzer 116. A distribution may beany suitable representation of the variation of keystroke eventintervals over a time period. In some embodiments, a distribution may bea histogram and include counts of keystroke event intervals withincertain ranges over a time period. A histogram may be constructed byidentifying a range of values for keystroke event intervals over a timeperiod and dividing the range into a series of small intervals, whichmay be referred to as “bins.” To generate the histogram, the analyzer118 may allocate each occurrence of a keystroke event interval over thetime period to one of the bins, which is the bin having an interval thatmatches the interval of that keystroke event. The analyzer 118 may thendetermine a number of occurrences for each bin. In some embodiments, adistribution may be continuous, such as a probability distributionfunction, rather than a discrete histogram. The probability distributionfunction may be identified by estimating a function based on a resultinghistogram. Though, it should be appreciated that these are merelyexamples and other types of statistical distributions and functions forcharacterizing time intervals may be used according to techniquesdescribed herein.

An exemplary analysis of keystroke events to determine a plurality ofdistributions of hold time by motor function analyzer 118 is illustratedin FIGS. 3A-3E. FIG. 3A illustrates variation in hold time values as aperson is typing. Individual hold time values may be determined byidentifying intervals of time when a key is pressed. Distributions ofhold time may be determined for time periods 301, 302, and 303. FIGS.3B-D illustrate exemplary distributions of hold time values ashistograms for time periods 301, 302, and 303, respectively. In thisexample, FIG. 3B illustrates a histogram corresponding to a distributionof hold times within time period 301 of FIG. 3A, and the high hold timevalue is shown as a count on the right of histogram in FIG. 3B.Similarly the histogram illustrated in FIG. 3C corresponds to adistribution of hold times within time period 302 of FIG. 3A, andhistogram illustrated in FIG. 3D corresponds to a distribution of holdtimes within time period 303. As distributions of hold times oversubsequent time periods are determined, multiple distributions may bedetermined by motor function input analyzer 118. These distributions maybe represented in a series, such as illustrated in FIG. 3E, where eachcolumn corresponds to values of histogram determined for one timeperiod. In FIG. 3E, the column on the left corresponds to the histogramvalues from time period 301, the next column corresponds to thehistogram values from time period 302, and the subsequent columncorresponds to the histogram values from time period 303. In thismanner, a set of distributions may be obtained over a time durationwhile a user is providing user input. The values of the set ofdistributions may be represented as a matrix where either the rows orcolumns indicated different distributions. As an example, a matrix, K,having j rows and i columns may include the values illustrated in FIG.3E, where each column corresponds to a different distribution (e.g., i=0corresponds to distribution from time period 301, i=2 corresponds todistribution from time period 302, and i=3 corresponds to distributionfrom time period 303).

Analysis of hold time values is provided as an example in FIGS. 3A-3E.It should be recognized that other types of time variables, such asflight time, press latency, and release latency, may be analyzed usingthese techniques to determine a plurality of distributions. Additionallyor alternatively, other types of distributions may be determined forcertain time periods, such as probability distributions, to determine aplurality of distributions.

Some embodiments relate to analyzing a plurality of distributions, suchas illustrated in FIG. 3E, to provide an indication of a user's motorfunction. The inventors have recognized that variation among a pluralityof distributions may provide an indication of a person's motor function.Variation among a plurality of distributions may include variation in anaverage value for each distribution of the plurality of distributions.Variation among a plurality of distributions may also include variationin distribution spread of the plurality of distributions. Bydistributions of a plurality, or by comparing a plurality ofdistributions with another plurality of distributions, informationrelated to a condition or status of a person's motor function may beidentified. When the distributions that are analyzed are spread over atime period, such as over a day, over a week, over a month, or over ayear, or other time period, a comparison may provide an indication ofchanges in the person's condition that correspond to progression of adisorder and/or effectiveness of a treatment. Additionally oralternatively, a person's condition may be assessed by comparing a setof distributions based on user input provided by the person to adifferent set of distributions based on user input provided by anotherindividual.

FIG. 4 illustrates different exemplary sets of distributions for acontrol individual having little or no motor function impairment(“Control”), a treated individual diagnosed with Parkinson's disease(“Treated PD”), and an untreated individual diagnosed with Parkinson'sdisease (“Untreated PD”). As shown in FIG. 4, the set of distributionsfor the control individual is characterized by limited variation indistribution spread and/or width among the distributions, while the setsof distributions for the treated PD individual and the untreated PDindividual have broader variations in spread. Additionally, an averagevalue may be identified for a set of distributions and provide acharacterization of a person's motor function. As shown in FIG. 4, thecontrol set of distributions has an average value lower than averagevalues for treated and untreated PD sets of distributions. FIG. 4 alsoillustrates that limited variation of the distributions for the controlindividual may indicate a high level of stability of the user's motorfunction, while the broader variation of the distributions for thetreated and untreated PD individuals may indicate a lower level ofstability of the user's motor function.

FIG. 4 also illustrates that distributions of keystroke event intervalsmay provide an indication of effectiveness of treatment for a conditionin improving motor function. A change in the variation amongdistributions (e.g., distribution spread or width) over time may providean indication of a change in the user's motor function. As shown in FIG.4, the distributions for an untreated individual has a broader spread orwidth of the distributions than for a person with Parkinson's diseaseundergoing treatment. By observing a decrease in the spread of thedistributions over time during the course of treatment, and thetreatment may be evaluated as reducing symptoms related to Parkinson'sdisease.

Further examples of sets of distributions acquired through thetechniques discussed above are provided in FIG. 5 which illustrates setsof distributions for three subjects each in a rested state and a stateof sleep inertia. Sleep inertia may be characterized as an impairedcognitive performance upon awakening. Individuals in a state of sleepinertia may have reduced motor impairment than when they are in a restedstate. By having a user perform typing tasks over a duration of time,distributions of time intervals may be determined. In FIG. 5,distributions of hold times are determined when each subject is in arested state and a state of sleep inertia. Variations among thedistributions may provide an indication of the subject's motor function.As an example, subject 7 while in a rested state has a set ofdistributions with little variation in spread among the distributions incomparison to the set of distributions for subject 7 while in a state ofsleep inertia. The motor function of subject 7 in a state of sleepinertia may be determined to be impaired based on the comparison.Similar comparisons may be performed for both subjects 2 and 11 toidentify increased motor impairment when the subjects are in a state ofsleep inertia.

As part of comparing one plurality of distributions to another pluralityof distributions, one or more features for a plurality distributions maybe identified to characterize the distributions. A feature may be one ormore values calculated from a plurality of distributions and may providean indication of a characteristic related to a user's motor function.Rather than visually comparing two sets of distributions, the one ormore features may provide a quantitative metric to compare. Analysis ofa plurality of distributions by motor function analyzer 118 may includeidentifying one or more features. A feature may provide an indication ofan average value of the distributions. For example, median values foreach distribution and an average value of the median values for theplurality of distributions may be determined. Another feature maycorrespond to variation in a spread of the plurality of distributions.Another feature may relate to the similarity of distributions within aplurality of distributions.

In some embodiments, a feature may include one or more characteristicsof at least a portion of the distributions. A characteristic may be amedian value, an average value, a peak value, a spread of thedistribution, or a width of a distribution. An average value of acharacteristic across multiple distributions may be determined. Theaverage value may provide an indication of stability of a user's motorfunction. As an example, high hold time values may correspond toimpaired motor function. Additionally, a feature may provide anindication of a user's motor function even when there are only a fewdata points available over a time period for a distribution. In someembodiments, identification of a feature may include calculating amedian value for a distribution of keystroke event intervals. Multiplemedian values may be calculated for each of a plurality ofdistributions. Identification of a feature may include calculating anaverage value by averaging the median values to obtain an average medianvalue for the plurality of distributions. In some embodiments, a featurecalculated from a plurality of distributions may be an average value ofmedian hold time values. An average value of median hold times mayprovide an indication of a user's motor function since motor impairmenttends to correspond to increased values for hold time. Any suitable typeof characteristic of the distributions may be averaged to provide anindication of a user's motor function. In some embodiments, a featuremay be calculated by averaging peak values of multiple distributions ofkeystroke event intervals.

As an example, an average value, K^(P), of a median values for aplurality of distributions, K, having z columns may be calculated basedon the following mathematical expression where K is a matrix having icolumns and j entries in each column:

$K^{P} = \frac{\sum\limits_{i = 0}^{z}\; {\arg \; \max \; K_{j,i}}}{z}$

In some embodiments, a portion of distributions may be used to determineone or more features may be determined to reduce including distributionswith an insufficient amount of information in the calculation. In someembodiments, a distribution may be selected for calculating one or morefeatures based on identifying a number of non-zero elements for thedistribution and comparing the number of non-zero elements to athreshold number. If the number of non-zero elements of a distributionis greater than the threshold number then, the distribution may beincluded as part of calculation of a feature for a set of distributions.

Some embodiments relate to calculating a feature indicating a degree ofstability among a plurality of distributions. Stability among aplurality of distributions may provide an indication of a user's motorfunction where a higher instability may indicate an impaired motorfunction. Identification of a feature that provides an indication ofstability across distributions may include comparing at least a firstdistribution to at least a second distribution to obtain a degree ofsimilarity. In some embodiments, a feature may be identified bycomparing each distribution of a plurality of distributions to eachother to obtain a degree of similarity among the plurality ofdistributions. Any suitable type of analysis to compare one distributionto another may be used including statistical correlation techniques(e.g., pairwise correlation), distance metrics, similarity metrics,and/or other comparisons techniques.

A feature indicating a degree of stability for a plurality ofdistributions may be determined by calculating a self-similarity matrixfor the plurality of distributions. In some embodiments, a portion ofthe plurality of distributions may be used in calculating theself-similarity matrix. Any suitable distance metric may be used incalculating a self-similarity matrix for a plurality of distributions.In some embodiments, a distance metric may calculate a variation betweentwo distributions by identifying a value representing differencesbetween the two distributions. As an example, a distance metric used forcalculating a difference between two distributions may be d(x, y)=∥x−y∥,where x and y are vectors representing the two distributions. A distancemetric for each pair combination of distributions may be calculated todetermine a self-similarity matrix. An example self-similarity matrix,S, is indicated by the following mathematical expression, where K_(t=n)is the distribution of the plurality of distributions at time period nand a distance metric is calculated for each entry in theself-similarity matrix:

$S = \begin{pmatrix}{d\left( {K_{t = 0},K_{t = 0}} \right)} & {d\left( {K_{t = 0},K_{t = 1}} \right)} & \ldots & {d\left( {K_{t = 0},K_{t = n}} \right)} \\\vdots & \vdots & \ddots & \vdots \\{d\left( {K_{t = n},K_{t = 0}} \right)} & {d\left( {K_{t = n},K_{t = 1}} \right)} & \ldots & {d\left( {K_{t = n},K_{t = n}} \right)}\end{pmatrix}$

The degree of variation within a self-similarity matrix may indicate adegree of stability for the plurality of distributions for a user. Morevariation within a self-similarity matrix may be indicated by higherdistance metrics. Higher distance metric values may also indicateimpairment of the user's motor function. In some embodiments, theself-similarity matrix, S, is normalized such that the range of valuesfor the self-similarity matrix cover a range of scalar values (e.g.,from 0 to 1). FIG. 6 illustrates visual representations of differentself-similarity matrices corresponding to sets of distributions based onuser input from three different subjects while each in a rested stateand a state of sleep inertia. Variations in greyscale indicate a valueof each distance metric from 0 to 1, where 0 indicates that there is novariation.

Some embodiments relate to a feature calculated by summing one or moredistance metrics determined for similarity matrix, such as theself-similarity matrix discussed above. Such a feature may provide anoverall indication of a degree of similarity for a plurality ofdistributions corresponding to variation among the plurality ofdistributions. Summed distance metric values for different sets ofdistributions may be compared to provide an indication of change in auser's motor function. For example, a higher summed distance metricsvalue may provide an indication of impaired user motor function.

It should be appreciated that other types of features calculated from aplurality of distributions may be used to provide an indication of auser's motor function, and aspects of the present application are notlimited to the exemplary features described herein.

Some embodiments relate to determining and comparing one plurality ofdistributions to another plurality of distributions to assess anindividual's motor function and/or monitor an individual's motorfunction. In some embodiments, one or more features for a plurality ofdistribution may be compared to one or more features of anotherplurality of distributions. The two pluralities of distributions may befrom the same individual or different individuals. One plurality ofdistributions may be provided as a control or reference used to comparethe other plurality of distributions. In some instances, the control orreference plurality of distributions may be from an earlier point intime than a current plurality of distributions. By comparing oneplurality of distributions to another plurality of distributions,changes to one or more features may be identified which may indicate adegree of motor function capabilities of an individual. In this manner,a plurality of distributions based on user input for a time duration mayserve as a biosignature for the user's motor function for the timeduration.

In some embodiments, one or more features for a plurality ofdistributions may be compared to one or more features for anotherplurality of distributions. A difference between one plurality ofdistributions and another plurality of distributions may be identifiedbased on comparing the one or more features for the two differentpluralities of distributions. In some embodiments, a feature related todistribution spread may be compared between a first plurality ofdistributions and a second plurality of distributions, and a change inthe user's motor function may be identified based on a result of thecomparison. In some embodiments, a score indicative of a user's motorfunction may be calculated based on comparing one or more features forthe two different pluralities of distributions.

Some embodiments relate to representing one or more features for aplurality of distributions as a vector by including values for the oneor more features as components of the vector. Comparison between a firstplurality of distributions and a second plurality of distributions mayinclude calculating a first vector for the first plurality ofdistributions and a second vector for the second plurality ofdistributions. A difference between the first vector and the secondvector may provide an indication of a change in the one or more featuresincluded as components of the first vector and the second vector.

In some embodiments, information on measurement data from one or moresensors such as movement, location, orientation, voice and/or pressuredata may be analyzed, and a feature related to the analyzed data mayprovide an indication a user's motor function. A feature related tosensor measurements may be tracked over time and may be combined withone or more features related to a plurality of distributions as part ofassessing a user's motor function. In some embodiments, a featurerelated to sensor measurements may be included as a component in afeature vector.

In some embodiments, a feature vector for a plurality of distributionsmay include features relating to an average value of median keystrokeevent intervals for the plurality of distributions. Determining theaverage value may include averaging a plurality of median values. Eachmedian value identifying a median keystroke event interval for adistribution in a plurality of distributions. The feature vector mayalso include a degree of similarity for the plurality of distributions,such as a value calculated by summing distance metrics from aself-similarity matrix as discussed above. A first vector having valuescorresponding to at least these two features of a first plurality ofdistributions may be compared to a second vector having valuescorresponding to at least these two features of a second plurality ofdistributions. A difference between the first vector and the secondvector may be calculated and may indicate a change in a user's motorfunction. The first vector and the second vector may be based on userinput from the same user at different times, providing an indication ofa change in the user's motor function over time.

An illustrative example of calculating feature vectors is shown withrespect to FIG. 7 which plots circular histograms indicating variationin average value of medians (as the x-component) and summed value ofdistance metrics from a self-similarity matrix (as the y-component).FIGS. 7A-D illustrate experimental results for quantifying fatigue via asleep inertia protocol. Individuals were awakened during the night toinduce sleep inertia and tested four times twice during the day andtwice during the night. Each test consisted of 15 minutes of typingdifferent text on different computers. FIGS. 7A and 7B illustrate adifference between a feature vector based on user input data when anindividual is in a rested state and a second feature vector based onuser input data when the individual is in a state of sleep inertia. Anindication of the difference between the two vectors is shown by line700 in FIG. 7A and line 702 in FIG. 7B. FIGS. 7C and 7D illustratelittle to no significant difference when comparing two feature vectorswhen the individual is in a similar state for both feature vectors, suchas during rested states as in FIG. 7C and during sleep inertia states asin FIG. 7D. FIGS. 7E and 7F plot average value of medians (along thex-axias) and summed value of distance metrics from a self-similaritymatrix (along the y-axis) for different sets of distributions fromindividuals during the day (+) and night (O). FIGS. 7E and 7F illustratean alternative representation of the data illustrated in FIGS. 7A-D.FIG. 7E illustrates higher values for both features (i.e. average valueof medians and summed value of distance metrics) during the night thanduring the day indicating that these features are representative ofimpaired motor function due to sleep inertia. FIG. 7F illustrates datasimilar to that shown in FIG. 7E, but the data is normalized to abaseline level for each individual. The lines in FIG. 7E identifychanges between day and night, illustrating that both these featuresincrease at night when individuals are woken to perform typing tasks.

In some embodiments a score for a plurality of distributions may bedetermined by analyzing one or more quantiles for a plurality ofdistributions. A first quantile and a second quantile may be determinedfor a distribution of the plurality of distributions. Keystroke eventintervals in the distributions may be identified as outliers based onthe first quantile. A number of outliers in the distribution may becalculated based on one or more identified outliers in the distribution.The number of outlier may be normalized by a number of key presses forthe distribution. A difference between the first quantile and the secondquantile, a standard deviation of the outliers, and a value based onpart of a covariance of the plurality of distributions may becalculated. A feature vector may be determined based on the number ofoutliers, the difference between the first and second quantiles, thestandard deviation of the outliers, and the value based on part of thecovariance. In some embodiments, a machine learning classifier orregressor (e.g., Support Vector Machine, Decision Trees, Neural Network)may output a score by learning examples of feature vectorsrepresentative of different levels of motor impairment and/or differentconditions that may exist in a person (e.g., neurological disorders orimpairments). The output score may be obtained over time by calculatingthe same feature vector and applying the classifier or regressorpreviously trained. The output score may be used to monitor changes in auser's motor function. FIGS. 8A and 8B illustrate representativeprofiles for scores determined by the machine learning classier based onindividuals with Parkinson's disease and control individuals.

The invention is useful in some aspects for detecting changes in aperson's “motor function” in order to collect information regarding avariety of conditions that may exist in the person, includingneurological disorders the person may be suffering from or otherneurological impairments such as brain injury (i.e. concussions), motorillnesses such as osteoarthritis, psychiatric conditions such aspersonality disorders, depression, anxiety, psychosis, developmentaldisorders, transient conditions such as intoxication, fatigue, stressand dehydration. This type of information can be useful, for instance,alone or in combination with other detection/diagnostic methods to aidin diagnosing a conditions, identifying early stages of disease,detecting changes in a condition influenced by environmental factorssuch as medicine or therapy, detecting temporary impairments associatedwith alcohol or drugs associated with psychomotor symptoms or fatigue,detecting early warnings of stroke or other debilitating diseases, whereearly intervention is critical.

Detection of neurodegenerative disease is an important utility of themethods and devices of the invention. Neurodegenerative diseasesencompass a variety of disorders that involve progressive loss ofstructure and/or function of neurons in affected regions of the nervoussystem, often accompanied by neuronal loss. Many of theneurodegenerative diseases that affect the brain can lead to dementia, adevastating condition in which the loss of cognitive abilitiesdetrimentally affects daily living and social functioning.

Neurodegenerative diseases include but are not limited to Parkinson'sDisease, Parkinsonism, forms of Dementia such as Alzheimer's Disease orMild Cognitive impairment, Multiple sclerosis, Amyotrophic lateralsclerosis. Neurological disease include, but are not limited to,Schizophrenia, Bipolar disorder, Autism, Epilepsy and Depression.Neurological impairments include brain injury (e.g. concussions), motorillnesses such as osteoarthritis, psychiatric conditions such aspersonality disorders, anxiety, psychosis, developmental disorders, andtransient conditions such as intoxication, fatigue, stress anddehydration.

The methods and devices described herein can be used to detectproperties associated with these disorders, often in earlier stages thanthe diseases can be detected by other methods. If a disease is detectedusing the methods of the invention, the presence of the disease or otherproperties such as the subtype or stage or degree of the disease can befurther assessed by alternative methods known in the art. Alternativelyif a disease is detected using other methodology it may be verified orfurther characterized using the methods of the invention. A number ofproperties associated with these diseases are well known in the art andmay be used for further characterization.

Parkinson's disease is pathologically characterized by the presence ofcytoplasmic Lewy bodies, major components of which are filamentscomposed of the neuronal protein alpha-synuclein, in neurons within thebrain. Alpha-synuclein aggregates have been associated with severalneurological diseases. A number of dominant point mutations inalpha-synuclein that cause familial early onset Parkinson's disease havebeen described. Duplication and triplication of the alpha-synucleingene, leading to overproduction of alpha-synuclein, have also beenlinked to familial early-onset Parkinson's disease. In vitro studieshave demonstrated that recombinant alpha-synuclein can form Lewybody-like fibrils that recapitulate the ultrastructural features ofalpha-synuclein aggregates isolated from patients with Parkinson'sdisease. Certain Parkinson's disease-linked alpha-synuclein mutationshave been shown to accelerate the aggregation process. Parkinson'sdisease is clinically characterized by bradykinesia, rigidity, restingtremor, and postural rigidity, a constellation of symptoms commonlyreferred to as “parkinsonism”. Patients frequently develop cognitiveimpairment and depression as the disease progresses. Most motor symptomscan be attributed to the degeneration of dopaminergic neurons within thesubstantia nigra pars compacta, a key regulatory nucleus of the basalganglia circuitry. However, several other nondopaminergic neuronalpopulations may also degenerate, including various autonomic nuclei andthe locus ceruleus as well as glutamatergic neurons throughout thecerebral cortex.

Alzheimer's disease is a neurodegenerative disorder characterized byneurofibrillary tangles and plaques containing an amyloid beta peptide.Patients with Alzheimer's disease exhibit progressive dementia, whichmay manifest with impairment in memory and cognitive abilities.Proteolytic cleavage of the amyloid precursor protein (APP) results inthe generation of an amyloid beta peptide ranging typically from 38 to43 amino acids long. The amyloid beta 1-42 peptide is particularly proneto self-aggregation and is strongly linked to development of Alzheimer'sdisease.

The methods and devices described herein may also be used to assess theeffectiveness of a therapeutic agent or putative therapeutic agent, bydetecting changes in the disease state or progression. The methods anddevices may also be used for evaluating or monitoring the state of thedisease. The terms “assessing”, “determining”, “evaluating”, “assaying”are used interchangeably herein to refer to any form of detection ormeasurement of a change, and include determining whether a change indegree of disease or condition, etc., is present or not. The result ofan assessment may be expressed in qualitative and/or quantitative terms.Assessing may be relative or absolute.

Amyotrophic lateral sclerosis (ALS), also called Lou Gehrig's disease,is a progressive, fatal neurological disease. ALS occurs when specificnerve cells in the brain and spinal cord that control voluntary movementgradually degenerate and causes the muscles under their control toweaken and waste away, leading to paralysis. Currently there is no curefor ALS; nor is there a proven therapy that will prevent or reverse thecourse of the disorder.

Currently, Parkinson's disease is treated with several differentcompounds and combinations, the effectiveness on a particular patient ofwhich could be further assessed using the methods of the invention.Levodopa (L-dopa), which is converted into dopamine in the brain, isoften given to restore muscle control. Perindopril, an ACE inhibitorthat crosses the blood-brain barrier, is used to improve patients' motorresponses to L-dopa. Carbidopa is administered with L-dopa in order todelay the conversion of L-dopa to dopamine until it reaches the brain,and it also lessens the side effects of L-dopa. Other drugs used inParkinson's disease treatment include dopamine mimickers Mirapex(pramipexole dihydrochloride) and Requip (ropinirole hydrochloride), andTasmar (tolcapone), a COMT inhibitor that blocks a key enzymeresponsible for breaking down levodopa before it reaches the brain.

Autism (also referred to as Autism Spectrum Disorder, or ASD) is adisorder that seriously impairs the functioning of individuals. It ischaracterized by self-absorption, a reduced ability to communicate withor respond to the outside world, rituals and compulsive phenomena, andmental retardation. Autistic individuals are also at increased risk ofdeveloping seizure disorders, such as epilepsy. While the actual causeof autism is unknown, it appears to include one or more genetic factors,as indicated by the fact that the concordance rate is higher inmonozygotic twins than in dizygotic twins, and may also involve immuneand environmental factors, such as diet, toxic chemicals and infections.

In some instances the neurological disorder is a neuropsychiatricdisorder, which refers to conditions or disorders that relate to thefunctioning of the brain and the cognitive processes or behavior.Neuropsychiatric disorders may be further classified based on the typeof neurological disturbance affecting the mental faculties. The term“neuropsychiatric disorder,” considered here as a subset of“neurological disorders,” refers to a disorder which may be generallycharacterized by one or more breakdowns in the adaptation process.

The invention is also useful for characterizing or detecting motorillnesses or musculoskeletal diseases. A “motor illness” or“musculoskeletal disease” as used herein refers to a disorder thataffects the body's muscles, joints, tendons, ligaments and nerves. Thesediseases include but are not limited to Osteoarthritis, Back pain,Rheumatoid arthritis, Osteoporosis, Septic arthritis, gout,Fibromyalgia, and Systemic lupus erythematosus (SLE).

Osteoarthritis (OA) is a common disorder resulting in a reduction inbone mass, affecting over 150 million people worldwide, making it one ofthe most prevalent diseases in the world (WHO, 2009). OA attacks bodyjoints, affecting productivity and quality of life, and is extremelydisabling to the patient. Current therapies only provide short term painand inflammation relief but afford no protection against the inevitablefurther degeneration of joint cartilage, the hallmark of end-stage OA.This results in complete joint dysfunction (including deterioration ofbone and other soft tissues), leading to the patient's need for jointreplacement. It is thus vital to further understand OA diseasemechanisms and to develop effective therapeutics and drug-deliverysystems for curing it. In addition to detecting OA in a patient, themethods of the invention may be used to evaluate the effectiveness ofputative therapeutics.

Drugs and alcohol can impair motor function. This type of impairment cancause serious problems such as overdose, violence, accidents and motorvehicle crashes. The methods and devices of the invention can be used todetect unacceptable levels of motor impairment due to drug or alcoholingestion.

Additionally, different people react differently to differenttherapeutic treatments. The methods and devices of the invention may beused to monitor an individual patients reaction to a particulartherapeutic. Detecting, for instance changes in motor function.

Having thus described several aspects of at least one embodiment of thisinvention, it is to be appreciated that various alterations,modifications, and improvements will readily occur to those skilled inthe art.

Such alterations, modifications, and improvements are intended to bepart of this disclosure, and are intended to be within the spirit andscope of the invention. Further, though advantages of the presentinvention are indicated, it should be appreciated that not everyembodiment of the invention will include every described advantage. Someembodiments may not implement any features described as advantageousherein and in some instances. Accordingly, the foregoing description anddrawings are by way of example only.

A motor function characterization system in accordance with thetechniques described herein may take any suitable form, as embodimentsare not limited in this respect. An illustrative implementation of acomputer system 900 that may be used in connection with some embodimentsis shown in FIG. 9. One or more computer systems such as computer system900 may be used to implement any of the functionality described above.The computer system 900 may include one or more processors 910 and oneor more computer-readable storage media (i.e., tangible, non-transitorycomputer-readable media), e.g., volatile storage 920 and one or morenon-volatile storage media 930, which may be formed of any suitable datastorage media. The processor 910 may control writing data to and readingdata from the volatile storage 920 and the non-volatile storage device930 in any suitable manner, as embodiments are not limited in thisrespect. To perform any of the functionality described herein, theprocessor 910 may execute one or more instructions stored in one or morecomputer-readable storage media (e.g., volatile storage 920 and/ornon-volatile storage 930), which may serve as tangible, non-transitorycomputer-readable media storing instructions for execution by theprocessor 910.

The above-described embodiments can be implemented in any of numerousways. For example, the embodiments may be implemented using hardware,software or a combination thereof. When implemented in software, thesoftware code can be executed on any suitable processor or collection ofprocessors, whether provided in a single computer or distributed amongmultiple computers. It should be appreciated that any component orcollection of components that perform the functions described above canbe generically considered as one or more controllers that control theabove-discussed functions. The one or more controllers can beimplemented in numerous ways, such as with dedicated hardware, or withgeneral purpose hardware (e.g., one or more processors) that isprogrammed using microcode or software to perform the functions recitedabove.

In this respect, it should be appreciated that one implementationcomprises at least one computer-readable storage medium (i.e., at leastone tangible, non-transitory computer-readable medium), such as acomputer memory (e.g., hard drive, flash memory, processor workingmemory, etc.), a floppy disk, an optical disk, a magnetic tape, or othertangible, non-transitory computer-readable medium, encoded with acomputer program (i.e., a plurality of instructions), which, whenexecuted on one or more processors, performs above-discussed functions.The computer-readable storage medium can be transportable such that theprogram stored thereon can be loaded onto any computer resource toimplement techniques discussed herein. In addition, it should beappreciated that the reference to a computer program which, whenexecuted, performs above-discussed functions, is not limited to anapplication program running on a host computer. Rather, the term“computer program” is used herein in a generic sense to reference anytype of computer code (e.g., software or microcode) that can be employedto program one or more processors to implement above-techniques.

The phraseology and terminology used herein is for the purpose ofdescription and should not be regarded as limiting. The use of“including,” “comprising,” “having,” “containing,” “involving,” andvariations thereof, is meant to encompass the items listed thereafterand additional items. Use of ordinal terms such as “first,” “second,”“third,” etc., in the claims to modify a claim element does not byitself connote any priority, precedence, or order of one claim elementover another or the temporal order in which acts of a method areperformed. Ordinal terms are used merely as labels to distinguish oneclaim element having a certain name from another element having a samename (but for use of the ordinal term), to distinguish the claimelements.

Having described several embodiments of the invention in detail, variousmodifications and improvements will readily occur to those skilled inthe art. Such modifications and improvements are intended to be withinthe spirit and scope of the invention. Accordingly, the foregoingdescription is by way of example only, and is not intended as limiting.The invention is limited only as defined by the following claims and theequivalents thereto.

1. A method of characterizing motor function of a user by analyzing aninput by the user to a user interface of at least one computing device,the method comprising: receiving a sequence of keystroke eventsindicating that the user pressed at least a portion of the userinterface over a time duration; determining, by at least one processor,a plurality of distributions of keystroke event intervals over at leastsome of the time duration, wherein each distribution of the plurality ofkeystroke distributions corresponds to a portion of the time duration,the plurality of distributions of keystroke event intervals comprises afirst distribution relating to keystroke events included in a firstportion of the time duration and a second distribution relating tokeystroke events included in a second portion of the time duration,wherein determining a distribution of the plurality of distributionscomprises identifying time intervals between keystroke events that occurwithin a corresponding portion of the time duration; and determining astability of the motor function of the user at least in part byanalyzing at least the first distribution and the second distribution todetermine variation of keystroke event intervals over the time duration.2. The method of claim 1, wherein: receiving the sequence of keystrokeevents comprises receiving a sequence of a plurality of key selectionevents; and identifying time intervals between keystroke eventscomprises identifying time intervals between key selection events of theplurality of key selection events.
 3. The method of claim 1, whereindetermining a stability of the motor function of the user comprisesanalyzing at least the first distribution and the second distribution todetermine a measure of variation in width of at least the firstdistribution and the second distribution.
 4. The method of claim 1,wherein analyzing at least the first distribution and the seconddistribution comprises: calculating at least one feature of the firstand second distributions; and determining variation of the at least onefeature between the first and second distributions.
 5. The method ofclaim 4, wherein: calculating the at least one feature comprisescalculating a median keystroke event interval for each distribution ofthe plurality of distributions; and analyzing at least the first andsecond distributions comprises calculating an average median keystrokeevent interval by averaging the median keystroke event intervals foreach distribution.
 6. The method of claim 4, wherein: calculating the atleast one feature comprises comparing at least the first distribution tothe second distribution to obtain a degree of similarity indicative ofthe variation among the plurality of distributions.
 7. The method ofclaim 4, wherein: calculating the at least one feature comprisescomparing each distribution of the plurality of distributions to eachdistribution of the plurality of distributions to obtain a degree ofsimilarity indicative of the variation among on the plurality ofdistributions.
 8. The method of claim 1, wherein: determining adistribution of the plurality of distributions comprises identifying aplurality of keystroke time intervals between keystroke events relatedto the user pressing a key of the user interface.
 9. The method of claim1, wherein: determining a distribution of the plurality of distributionscomprises identifying a plurality of keystroke time intervals betweenkeystroke events related to the user pressing a key of the userinterface and a subsequent key of the user interface.
 10. The method ofclaim 1, wherein: determining a distribution of the plurality ofdistributions comprises identifying a plurality of keystroke timeintervals between keystroke events related to the user pressing a firstkey of the plurality of keys and a second key of the plurality of keysbefore releasing the first key.
 11. The method of claim 1, wherein thefirst portion of the time duration and the second portion of the timeduration are non-overlapping portions of the time duration.
 12. Themethod of claim 1, wherein: receiving the sequence of keystroke eventscomprises receiving a sequence of keystroke events input by the userwhile interacting with a plurality of different applications executingon the at least one computing device.
 13. The method of claim 1,wherein: receiving the sequence of keystroke events comprises receivinga sequence of keystroke events input by the user with a plurality ofprocesses executing on the at least one computing device.
 14. The methodof claim 1, further comprising: receiving a second sequence of keystrokeevents indicating that the user pressed at least a portion of the userinterface over a second time duration; determining, by the at least oneprocessor, a second plurality of distributions of keystroke eventintervals over at least some of a second time duration, wherein eachdistribution of the second plurality of keystroke distributionscorresponds to a portion of the second time duration, the secondplurality of distributions of keystroke event intervals comprises athird distribution relating to keystroke events included in a firstportion of the second time duration and a fourth distribution relatingto keystroke events included in a second portion of the second timeduration, wherein determining a second distribution of the plurality ofdistributions comprises identifying time intervals between keystrokeevents that occur within a corresponding portion of the second timeduration; and determining a second stability of the motor function ofthe user at least in part by analyzing at least the third distributionand the fourth distribution to determine variation of keystroke eventintervals over the second time duration.
 15. The method of claim 14,further comprising: identifying a change, if any, in the user's motorfunctions between the time duration and the second time duration bycomparing the plurality of distributions to the second plurality ofdistributions. 16.-32. (canceled)
 33. An apparatus comprising: a userinterface; and control circuitry configured to perform a methodcomprising: receiving a sequence of keystroke events indicating that theuser pressed at least a portion of the user interface over a timeduration; determining a plurality of biosignatures indicative of theuser's motor function at different times by determining, for abiosignature of the plurality of biosignatures, a plurality ofdistributions of keystroke event intervals over at least some of thetime duration, wherein each distribution of the plurality of keystrokedistributions corresponds to a portion of the time duration; andmonitoring motor function in the user by tracking a condition of theuser's motor function over time based on comparing a first biosignatureof the plurality of biosignatures with a second bio signature.
 34. Theapparatus of claim 33, wherein comparing the first biosignature of theplurality of biosignatures with the second biosignature comprisesidentifying a first stability level for the first biosignature and asecond stability level for the second biosignature and determining adifference between the first stability level and the second stabilitylevel.
 35. The apparatus of claim 34, wherein determining the differencebetween the first stability level and the second stability levelindicates a decrease in stability and the tracked condition indicatesimpairment of the user's motor function over time.
 36. The apparatus ofclaim 33, wherein the control circuitry is further configured to issue areport related to the tracked condition to the user.
 37. The apparatusof claim 33, wherein the control circuitry is further configured toissue a report related to the tracked condition to a medical provider.38-40. (canceled)
 41. A method of characterizing motor function of auser by analyzing an input by the user to a user interface of at leastone computing device, the method comprising: receiving a sequence ofkeystroke events indicating that the user pressed at least a portion ofthe user interface over a time duration; determining a plurality ofbiosignatures indicative of the user's motor function at different timesby determining, for a biosignature of the plurality of biosignatures, aplurality of distributions of keystroke event intervals over at leastsome of the time duration, wherein each distribution of the plurality ofkeystroke distributions corresponds to a portion of the time duration;and monitoring motor function in the user by tracking a condition of theuser's motor function over time based on comparing a first biosignatureof the plurality of biosignatures with a second biosignature.
 42. Themethod of claim 41, further comprising: identifying impairment in theuser's motor function over time based on monitoring the user's motorfunction; and comparing a characterization of the user's motor functionto characterizations of motor function associated with a plurality ofconditions and, when the characterization of the user's motor functionmatches a characterization associated with a condition, storing anindication that the user may have the condition. 43-49. (canceled)
 50. Amethod for characterizing a psychomotor impairment in a user comprising:detecting a plurality of keystroke events as a background task while theuser is interacting with a device sensitive to touch, determining timeintervals associated with the keystroke events, and characterizing apsychomotor impairment in the user based on the time intervalsassociated with the keystroke events.
 51. A method for early detectionof a neurological disease in a user comprising: detecting a plurality ofkeystroke events while the user is interacting with a device sensitiveto touch, determining time intervals associated with the keystrokeevents, and identifying the presence of a neurological disease in theuser prior to diagnosis of physical symptoms in the user.
 52. Anelectronic device comprising: a tactile interface for receiving aplurality of keystrokes, a processor configured to receive user inputfrom the tactile interface on the plurality of keystrokes, and a storagemedium storing processor executable instructions that when executed bythe processor perform a method comprising determining a plurality ofdistributions of keystroke event intervals over at least some of thetime duration, wherein each distribution of the plurality of keystrokedistributions corresponds to a portion of the time duration, theplurality of distributions of keystroke event intervals comprises afirst distribution relating to keystroke events included in a firstportion of the time duration and a second distribution relating tokeystroke events included in a second portion of the time duration,wherein determining a distribution of the plurality of distributionscomprises identifying time intervals between keystroke events that occurwithin a corresponding portion of the time duration; and analyzing atleast the first distribution and the second distribution to determinevariation of keystroke event intervals over the time duration.
 53. Thedevice of claim 52, wherein the electronic device is a computer.
 54. Thedevice of claim 52, wherein the electronic device is a tablet.
 55. Thedevice of claim 52, wherein the electronic device is a touch screen.